Optimizing Top K in Postgres

· · 来源:tutorial百科

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An alternative evaluation approach would be to provide the retrieved documents into a reasoning model and check whether it produces the correct answer end-to-end. We deliberately avoid this for two reasons. First, it confounds search quality with reasoning quality: if the downstream model fails to answer correctly, it is ambiguous whether the search agent retrieved insufficient evidence or the reasoning model failed to use what was provided. Final answer found isolates the search agent's contribution — if a document containing the answer appears in the output set, the retrieval succeeded regardless of the downstream models performance. This separation is further justified by benchmarks like BrowseComp-Plus, where oracle performance given all supporting documents is high, indicating that the accuracy bottleneck on this style of task is search rather than reasoning. Second, keeping a reasoning model out of the loop is practical: during RL training, every rollout would require an additional LLM call per episode, adding cost and latency that scale with the number of trajectories per step.

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